Our method integrates the result of a state-of-the-art ML model (that identifies the probability of being suspicious, equivalent to the 1 - reputation score) [1]} with Algorand's consensus algorithm. In Algorand, to achieve consensus, each node performs several steps (cf. Appendix ). In the first step, a node depending on certain criteria, forms and proposes a block. This block is then validated by other nodes (called validators, selected based on certain criteria) in multiple steps powered by a verifiable random function (VRF). We parameterize the received output of the VRF with the reputation score to make the decision on accepting or rejecting a block proposal. The votes of the validators are changed in accordance with their respective reputation score. This process happens at each node with no additional communication of any extra information. Each user makes a decision based on the reputation scores unilaterally and then the cumulative effect of all such decisions leads to the outcome of consensus round. Our methodology restricts the criminal entities and, in the process, freezes their accounts in a way that no transactions from such entities will get approved by an honest validator. We explain our proposed method in detail in section .
